OpenRaiser/Intern-Atlas huggingface dataset:
Streaming Balanced Clustering
Venue: arXiv cs.DS 2019
Year: 2019

Clustering of data points in metric space is among the most fundamental problems in computer science with plenty of applications in data mining, information retrieval and machine learning. Due to the necessity of clustering of large datasets, several streaming algorithms have been developed for different variants of clustering problems such as $k$-median and $k$-means problems. However, despite the importance of the context, the current understanding of balanced clustering (or more generally capacitated clustering) in the streaming setting is very limited. The only previously known streaming approximation algorithm for capacitated clustering requires three passes and only handles insertions. In this work, we develop \emph{the first single pass streaming algorithm} for a general class of clustering problems that includes capacitated $k$-median and capacitated $k$-means in Euclidean space, using only poly$( k d \log Δ)$ space, where $k$ is the number of clusters, $d$ is the dimension and $Δ$ is the maximum relative range of a coordinate. (Note that $d\log Δ$ is the space required to represent one point.) This algorithm only violates the capacity constraint by a $1+ε$ factor. Interestingly, unlike the previous algorithm, our algorithm handles both insertions and deletions of points. To provide this result we define a decomposition of the space via some curved half-spaces. We used this decomposition to design a strong coreset of size poly$( k d \log Δ)$ for balanced clustering. Then, we show that this coreset is implementable in the streaming and distributed settings.
On the Hardness of Set Disjointness and Set Intersection with Bounded Universe
Venue: arXiv cs.DS 2019
Year: 2019

In the SetDisjointness problem, a collection of $m$ sets $S_1,S_2,...,S_m$ from some universe $U$ is preprocessed in order to answer queries on the emptiness of the intersection of some two query sets from the collection. In the SetIntersection variant, all the elements in the intersection of the query sets are required to be reported. These are two fundamental problems that were considered in several papers from both the upper bound and lower bound perspective. Several conditional lower bounds for these problems were proven for the tradeoff between preprocessing and query time or the tradeoff between space and query time. Moreover, there are several unconditional hardness results for these problems in some specific computational models. The fundamental nature of the SetDisjointness and SetIntersection problems makes them useful for proving the conditional hardness of other problems from various areas. However, the universe of the elements in the sets may be very large, which may cause the reduction to some other problems to be inefficient and therefore it is not useful for proving their conditional hardness. In this paper, we prove the conditional hardness of SetDisjointness and SetIntersection with bounded universe. This conditional hardness is shown for both the interplay between preprocessing and query time and the interplay between space and query time. Moreover, we present several applications of these new conditional lower bounds. These applications demonstrates the strength of our new conditional lower bounds as they exploit the limited universe size. We believe that this new framework of conditional lower bounds with bounded universe can be useful for further significant applications.
Clinical Text Generation through Leveraging Medical Concept and Relations
Venue: arXiv cs.CL 2019
Year: 2019

With a neural sequence generation model, this study aims to develop a method of writing the patient clinical texts given a brief medical history. As a proof-of-a-concept, we have demonstrated that it can be workable to use medical concept embedding in clinical text generation. Our model was based on the Sequence-to-Sequence architecture and trained with a large set of de-identified clinical text data. The quantitative result shows that our concept embedding method decreased the perplexity of the baseline architecture. Also, we discuss the analyzed results from a human evaluation performed by medical doctors.
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Venue: arXiv cs.CV 2019
Year: 2019

Deep video recognition is more computationally expensive than image recognition, especially on large-scale datasets like Kinetics [1]. Therefore, training scalability is essential to handle a large amount of videos. In this paper, we study the factors that impact the training scalability of video networks. We recognize three bottlenecks, including data loading (data movement from disk to GPU), communication (data movement over networking), and computation FLOPs. We propose three design guidelines to improve the scalability: (1) fewer FLOPs and hardware-friendly operator to increase the computation efficiency; (2) fewer input frames to reduce the data movement and increase the data loading efficiency; (3) smaller model size to reduce the networking traffic and increase the networking efficiency. With these guidelines, we designed a new operator Temporal Shift Module (TSM) that is efficient and scalable for distributed training. TSM model can achieve 1.8x higher throughput compared to previous I3D models. We scale up the training of the TSM model to 1,536 GPUs, with a mini-batch of 12,288 video clips/98,304 images, without losing the accuracy. With such hardware-aware model design, we are able to scale up the training on Summit supercomputer and reduce the training time on Kinetics dataset from 49 hours 55 minutes to 14 minutes 13 seconds, achieving a top-1 accuracy of 74.0%, which is 1.6x and 2.9x faster than previous 3D video models with higher accuracy. The code and more details can be found here: http://tsm-hanlab.mit.edu.
Reviewing National Cybersecurity Awareness for Users and Executives in Africa
Venue: arXiv cs.CY 2019
Year: 2019

There is an unprecedented increase in cybercrime globally observed over the last years. One of the regions driving this increase is Africa, where significant financial losses are reported. Yet, citizens of African countries are not aware of the risks present in cyberspace. The design and implementation of national awareness campaigns by African countries to address this problem are in their infancy state, mainly due to the absence of capacity building efforts. As part of the Global Cybersecurity Capacity Centre (GCSCC) programme, we conducted a series of focus groups in six African countries, in order to assess their cybersecurity posture, a critical component of which is user and executive awareness of cyber risks. This paper is an extended version of previous work where an initial analysis of awareness for cyber risk in African countries was presented. In this extended version, we reflect on best practice approaches for developing national awareness campaigns and use these as a framework to analyse qualitative data from the focus groups. We discuss the current state of African countries with regards to the implementation of national cybersecurity awareness campaigns for. users and executives, the main obstacles in combating cybercrime and conclude with recommendations on how African countries can identify and prioritise activities to increase their capacity regarding cybersecurity awareness.
Unsupervised Doodling and Painting with Improved SPIRAL
Venue: arXiv cs.CV 2019
Year: 2019

We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is trained with rewards provided by a discriminator network simultaneously trained to assess the realism of the agent's samples, either unconditional or reconstructions. Compared to prior work, we make a number of improvements to the architectures of the agents and discriminators that lead to intriguing and at times surprising results. We find that when sufficiently constrained, generative agents can learn to produce images with a degree of visual abstraction, despite having only ever seen real photographs (no human brush strokes). And given enough time with the painting environment, they can produce images with considerable realism. These results show that, under the right circumstances, some aspects of human drawing can emerge from simulated embodiment, without the need for external supervision, imitation or social cues. Finally, we note the framework's potential for use in creative applications.
Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IM/DD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42\,Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.
Automotive Cybersecurity: Foundations for Next-Generation Vehicles
Venue: arXiv cs.CR 2019
Year: 2019

The automotive industry is experiencing a serious transformation due to a digitalisation process and the transition to the new paradigm of Mobility-as-a-Service. The next-generation vehicles are going to be very complex cyber-physical systems, whose design must be reinvented to fulfil the increasing demand of smart services, both for safety and entertainment purposes, causing the manufacturers' model to converge towards that of IT companies. Connected cars and autonomous driving are the preeminent factors that drive along this route, and they cause the necessity of a new design to address the emerging cybersecurity issues: the "old" automotive architecture relied on a single closed network, with no external communications; modern vehicles are going to be always connected indeed, which means the attack surface will be much more extended. The result is the need for a paradigm shift towards a secure-by-design approach.
Concept Drift Detection and Adaptation with Weak Supervision on Streaming Unlabeled Data
Venue: arXiv cs.LG 2019
Year: 2019

Concept drift in learning and classification occurs when the statistical properties of either the data features or target change over time; evidence of drift has appeared in search data, medical research, malware, web data, and video. Drift adaptation has not yet been addressed in high dimensional, noisy, low-context data such as streaming text, video, or images due to the unique challenges these domains present. We present a two-fold approach to deal with concept drift in these domains: a density-based clustering approach to deal with virtual concept drift (change in statistical properties of features) and a weak-supervision step to deal with real concept drift (change in statistical properties of target). Our density-based clustering avoids problems posed by the curse of dimensionality to create an evolving 'map' of the live data space, thereby addressing virtual drift in features. Our weak-supervision step leverages high-confidence labels (oracle or heuristic labels) to generate weighted training sets to generalize and update existing deep learners to adapt to changing decision boundaries (real drift) and create new deep learners for unseen regions of the data space. Our results show that our two-fold approach performs well with >90% precision in 2018, four years after initial deployment in 2014, without any human intervention.
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations
Venue: arXiv cs.CL 2019
Year: 2019

Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models. Thus, we investigate and challenge several aspects of BERT's commonsense representation abilities. First, we probe BERT's ability to classify various object attributes, demonstrating that BERT shows a strong ability in encoding various commonsense features in its embedding space, but is still deficient in many areas. Next, we show that, by augmenting BERT's pretraining data with additional data related to the deficient attributes, we are able to improve performance on a downstream commonsense reasoning task while using a minimal amount of data. Finally, we develop a method of fine-tuning knowledge graphs embeddings alongside BERT and show the continued importance of explicit knowledge graphs.
Toward Edge-enabled Cyber-Physical Systems Testbeds
Venue: arXiv cs.DC 2019
Year: 2019

The use of edge computing can be extremely valuable in support of CPS efforts. However, few if any testbeds provide the type of resource control and provisioning required to support edge-enabled CPS experimentation. Likewise, commercial offerings provide operational capabilities, but lack the distributed infrastructure and transparency provided by research testbed. In this paper we propose methods to develop new and augment existing testbeds to better support the challenges of edge computing and CPS research. The proposed network is specifically designed to address the challenges associated with edge-based provisioning, data collection, analysis, monitoring, and measurement across islands of edge and data center resources. We present the purpose of our work, the basic architecture, initial results, the relationship to the existing software, and the potential of an existing edge-focused framework to support the foundations of edge-focused CPS testbeds.
DeepMark: One-Shot Clothing Detection
Venue: arXiv cs.CV 2019
Year: 2019

The one-shot approach, DeepMark, for fast clothing detection as a modification of a multi-target network, CenterNet, is proposed in the paper. The state-of-the-art accuracy of 0.723 mAP for bounding box detection task and 0.532 mAP for landmark detection task on the DeepFashion2 Challenge dataset were achieved. The proposed architecture can be used effectively on the low-power devices.
Can Automated Program Repair Refine Fault Localization?
Venue: arXiv cs.SE 2019
Year: 2019

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, we observe that in the literature their only connection is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we explore their connection in the other direction, i.e., can program repair in turn help with fault localization? In this way,we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed).We have designed ProFL, a simplistic approach using patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning.
Sit-to-Stand Analysis in the Wild using Silhouettes for Longitudinal Health Monitoring
Venue: arXiv cs.CV 2019
Year: 2019

We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery.
GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework for Autonomous Systems
Venue: arXiv cs.AI 2019
Year: 2019

While AI techniques have found many successful applications in autonomous systems, many of them permit behaviours that are difficult to interpret and may lead to uncertain results. We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems. We give a new formulation of Goal Task Network (GTN) that is tailored for our model checking based framework. We then provide a systematic method that models GTNs in the model checker Process Analysis Toolkit (PAT). We present our planning and goal reasoning system as a framework called Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in an uncertain environment. Finally, we demonstrate the proposed ideas in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.
Silas: High Performance, Explainable and Verifiable Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service. A focus of Silas is on providing a formal foundation of decision trees in order to support logical analysis and verification of learned prediction models. This paper describes the distinct features of Silas: The Model Audit module formally verifies the prediction model against user specifications, the Enforcement Learning module trains prediction models that are guaranteed correct, the Model Insight and Prediction Insight modules reason about the prediction model and explain the decision-making of predictions. We also discuss implementation details ranging from programming paradigm to memory management that help achieve high-performance computation.
A General Upper Bound for Unsupervised Domain Adaptation
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, a theory proposed by Ben-David et al. (2010) provides a upper bound for target error when transferring the knowledge, which can be summarized as minimizing the source error and distance between marginal distributions simultaneously. However, common methods based on the theory usually ignore the joint error such that samples from different classes might be mixed together when matching marginal distribution. And in such case, no matter how we minimize the marginal discrepancy, the target error is not bounded due to an increasing joint error. To address this problem, we propose a general upper bound taking joint error into account, such that the undesirable case can be properly penalized. In addition, we utilize constrained hypothesis space to further formalize a tighter bound as well as a novel cross margin discrepancy to measure the dissimilarity between hypotheses which alleviates instability during adversarial learning. Extensive empirical evidence shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks.
Analyzing the Spotify Top 200 Through a Point Process Lens
Venue: arXiv cs.SI 2019
Year: 2019

Every generation throws a hero up the pop charts. For the current generation, one of the most relevant pop charts is the Spotify Top 200. Spotify is the world's largest music streaming service and the Top 200 is a daily list of the platform's 200 most streamed songs. In this paper, we analyze a data set collected from over 20 months of these rankings. Via exploratory data analysis, we investigate the popularity, rarity, and longevity of songs on the Top 200 and we construct a stochastic process model for the daily streaming counts that draws upon ideas from stochastic intensity point processes and marked point processes. Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.
Slanted Stixels: A way to represent steep streets
Venue: arXiv cs.CV 2019
Year: 2019

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a Fully Convolutional Network (FCN), which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.